Finding Efficient Linguistic Feature Set for Authorship Verification

Linguistic Profiling for Authorship Recognition and Verification

A new technique is introduced, linguistic profiling, in which large numbers of counts of linguistic features are used as a text profile, which can then be compared to average profiles for groups of texts. The technique proves to be quite effective for authorship verification and recognition. The best parameter settings yield a False Accept Rate of 8.1% at a False Reject Rate equal to zero for t...

Authorship Verification, combining Linguistic Features and Different Similarity Functions

Authorship analysis is an important task for different text applications, for example in the field of digital forensic text analysis. Hence, we propose an authorship analysis method that compares the average similarity of a text of unknown authorship with all the texts of an author. Using this idea, a text that was not written by an author, would not exceed the average of similarity with known ...

An Efficient Algorithm for e-mail Authorship Verification

Authorship Identification Using a Reduced Set of Linguistic Features

The proposed solution for authorship attribution combines a couple of the most important features identified in previous research in this domain with classification algorithms in order to detect the correct author. We consider that the most relevant aspect of our work is the small number of linguistic features and the use of the same framework to solve both the open and the closed class authors...

Authorship Attribution Based on Feature Set Subspacing Ensembles

Authorship attribution can assist the criminal investigation procedure as well as cybercrime analysis. This task can be viewed as a single-label multi-class text categorization problem. Given that the style of a text can be represented as mere word frequencies selected in a language-independent method, suitable machine learning techniques able to deal with high dimensional feature spaces and sp...